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The Untargeted Metabolomics Reveals Differences in Energy Metabolism in Patients with Different Subtypes of Ischemic Stroke

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Abstract

Aims

Ischemic stroke (IS) is the most common subtype of stroke. The risk factors and pathogenesis of IS are complex and varied due to different subtypes. Therefore, we used metabolomics technology to investigate the biomarkers and potential pathophysiological mechanisms of different subtypes of IS.

Methods

We included 126 IS patients and divided them into two groups based on the TOAST classification: large-artery atherosclerosis (LAA) group (n = 87) and small-vessel occlusion (SVO) group (n = 39). Plasma metabolomics analysis was performed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to identify metabolic profiles in LAA and SVO subtype IS patients and to determine metabolic differences between patients with the two subtypes of IS.

Results

We identified 26 differential metabolites between LAA and SVO subtype IS. A multiple prediction model based on the plasm metabolites had good predictive ability for IS subtyping (AUC = 0.822, accuracy = 77.8%), with 12,13-DHOME being the most important differential metabolite in the model. The differential metabolic pathways between the two subtypes of IS patients included tricarboxylic acid (TCA) cycle, alanine, aspartate and glutamate metabolism, and pyruvate metabolism, mainly focused on energy metabolism.

Conclusion

12,13-DHOME emerged as the primary discriminatory metabolite between LAA and SVO subtypes of IS. In LAA subtype IS patients, energy metabolism, encompassing pyruvate metabolism and the TCA cycle, exhibited lower activity levels when compared to patients with the SVO subtype IS. The utilization of targeted metabolomics holds the potential to improve diagnostic accuracy for distinguishing stroke subtypes.

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Data Availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

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Acknowledgements

Statistical analysis was conducted using SPSS 22.0 and web-based software MetaboAnalyst 5.0 (https://www.metaboanalyst.ca).

Funding

This work was supported by the National Key Research and Development Projects (2022YFC3602400, 2022YFC3602401), the National Natural Science Foundation of China (82271369, 82101407) and the Natural Science Foundation of Hunan Province (2021JJ31109).

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Authors and Affiliations

  1. Department of Neurology, Xiangya Hospital, Central South University, 87# Xiangya Road, Changsha, 410008, Hunan, China

    Xi Li, Jiaxin Li, Fang Yu, Xianjing Feng, Yunfang Luo, Zeyu Liu, Tingting Zhao & Jian Xia

  2. Clinical Research Center for Cerebrovascular Disease of Hunan Province, Central South University, Changsha, Hunan, China

    Jian Xia

  3. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China

    Jian Xia

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  1. Xi Li
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Contributions

Study design: Xi Li, Jiaxin Li; data collection and analysis: Xi Li, Jiaxin Li, Fang Yu, Xianjing Feng, Yunfang Luo, Zeyu Liu, Tingting Zhao; writing: Xi Li, Jiaxin Li; funding: Jian Xia; administration: Jian Xia.

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Correspondence to Jian Xia.

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The studies involving human participants were reviewed and approved by Xiangya Hospital Ethics Committee. The patients/participants provided their written informed consent to participate in this study.

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Xi Li and Jiaxin Li contributed equally to this work and share first authorship.

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Li, X., Li, J., Yu, F. et al. The Untargeted Metabolomics Reveals Differences in Energy Metabolism in Patients with Different Subtypes of Ischemic Stroke. Mol Neurobiol (2024). https://doi.org/10.1007/s12035-023-03884-w

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